The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

Abstract. Recent increases in oil and natural gas (NG) production throughout the western US have come with scientific and public interest in emission rates, air quality and climate impacts related to this industry. This study uses a regionalscale air quality model (WRF-Chem) to simulate high ozone (O 3 ) episodes during the winter of 2013 over the Uinta Basin (UB) in northeastern Utah, which is densely populated by thousands of oil and NG wells. The high-resolution meteorological simulations are able qualitatively to reproduce the wintertime cold pool conditions that occurred in 2013, allowing the model to reproduce the observed multi-day buildup of atmospheric pollutants and the accompanying rapid photochemical ozone formation in the UB.Two different emission scenarios for the oil and NG sector were employed in this study. The first emission scenario (bottom-up) was based on the US Environmental Protection Agency (EPA) National Emission Inventory (NEI) (2011, version 1) for the oil and NG sector for the UB. The second emission scenario (top-down) was based on estimates of methane (CH 4 ) emissions derived from in situ aircraft measurements and a regression analysis for multiple species relative to CH 4 concentration measurements in the UB. Evaluation of the model results shows greater underestimates of CH 4 and other volatile organic compounds (VOCs) in the simulation with the NEI-2011 inventory than in the case when the top-down emission scenario was used. Unlike VOCs, the NEI-2011 inventory significantly overestimates the emissions of nitrogen oxides (NO x ), while the topdown emission scenario results in a moderate negative bias. The model simulation using the top-down emission case captures the buildup and afternoon peaks observed during high O 3 episodes. In contrast, the simulation using the bottomup inventory is not able to reproduce any of the observed high O 3 concentrations in the UB. Simple emission reduction scenarios show that O 3 production is VOC sensitive and NO x insensitive within the UB. The model results show a disproportionate contribution of aromatic VOCs to O 3 formation relative to all other VOC emissions. The model analysis reveals that the major factors driving high wintertime O 3 in the UB are shallow boundary layers with light winds, high emissions of VOCs from oil and NG operations compared to NO x emissions, enhancement of photolysis fluxes and reduction of O 3 loss from deposition due to snow cover.

For assessing the impacts of wind farms on regional climate, wind farms may be represented in climate models by an increase in aerodynamic roughness length. Studies employing this method have found near-surface temperature changes of 1–2 K over wind farm areas. By contrast, mesoscale and large-eddy simulations (LES), which represent wind farms as elevated sinks of momentum, generally showed temperature changes of less than 0.5 K. This study directly compares the two methods of representing wind farms in simulations of a strong diurnal cycle. Nearly the opposite wake structure is seen between the two methods, both during the day and at night. The sensible heat fluxes are generally exaggerated in the enhanced roughness approach, leading to much greater changes in temperature. Frequently, the two methods display the opposite sign in temperature change. Coarse resolution moderates the sensible heat fluxes but does not significantly improve the near-surface temperatures or low-level wind speed deficit. Since wind farm impacts modeled by the elevated momentum sink approach are similar to those seen in observations and from LES, the authors conclude that the increased surface roughness approach is not an appropriate option to represent wind farms or explore their impacts.